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Azure Data Engineer Training in Canada
Azure data engineering training in Canada offered by Version IT focuses on practical learning and real-world implementation of Azure data technologies.
Understanding Azure Data Engineer Training in Canada
The transition of the world to cloud computing has opened vast opportunities to the employees who are aware of the current data technologies. The organizations today produce huge amounts of information on a daily basis, and they require strong cloud systems that would allow them to store, process, and analyze such data efficiently. Microsoft Azure is now considered to be one of the most reliable cloud ecosystems to develop scalable data engineering solutions.
Companies in Canada are quickly migrating to data platforms that are based on Azure to facilitate analytics, machine learning, and enterprise applications. Consequently, there is high demand of skilled professionals in cloud data. The Azure data engineering courses in Canada enable graduates to acquire the skills needed to develop the effective cloud data systems and to develop their careers in the sphere of IT.
As an IT expert with the intention to specialise in cloud technologies or as a novice in the field of data engineering looking to explore the field, the training of Microsoft Azure data engineering training in Canada offers a great opportunity to get in one of the most rapidly developing fields in technology.
Why Azure Data Engineering is a Top Career Choice in Canada
Azure Data Engineers are professionals who develop pipelines of data that are reliable and oversee cloud data infrastructure and cross-platform flows of data. In Canada, they are trained to cope with such critical responsibilities.
Following factors among the drivers of the demand of Azure Data Engineers are:
- Penetration of cloud computing by Canadian organizations
- Active development of data analytics and artificial intelligence technologies
- Growth of digital transformation projects
- Necessity of secure and scalable cloud data infrastructure
- Demand for specialists knowledgeable in Azure data platforms are among
Azure engineering course in Canada enables professionals to acquire the knowledge needed to operate with advanced technologies and facilitate data-driven business processes.
Core Capabilities Developed During the Training
A properly designed Microsoft Azure data engineering training in Canada aims at equipping the intended students with the technical skills needed to operate large scale data environments on cloud.
Noteworthy skills acquired by learners are:
- Scaling the data pipelines with the help of Azure technologies.
- The combination of structured and unstructured data.
- Controlling cloud storage applications.
- Facilitating and designing data into analytics systems.
- Promoting business intelligence and reporting applications.
- Performance and reliability in cloud data workflow optimization.
The skills will enable the learners to apply cloud data solutions in real business settings with confidence.
Our Azure data engineering course in Canada makes sure that learners receive a proper exposure to the current data engineering processes employed in the top organizations.
Advantages of Azure Engineering Online Training in Canada
It will need flexibility to learn high level technical skills, particularly to the working population and students who are carrying a lot of activities. Version IT is a company that provides online training in engineering (Azure) in Canada so that the learners can take the high-quality training anywhere.
The advantages of our Azure engineering online training in Canada are:
- Live online courses which are offered by professional trainers.
- Live interactive learning sessions and discussions.
- The right of access to recordings and research materials.
- Flexible work hours to fit working people.
- Access to learners in the industry irrespective of geographical location.
Through training on Azure data engineering in Canada, the professionals will have an opportunity to acquire useful technical knowledge working at home or office.
Why Choose Version IT for Azure Data Engineer Training in Canada
Version IT has established a good reputation towards providing professional IT training programs which are in line with industry requirement. The Azure data engineering course in Canada provided by Version IT addresses:
Industry Expert Trainers
Version IT trainers have practical skills in Azure cloud solutions and enterprise data engineering. Their instruction assists learners to comprehend practices of implementation.
Practical Training
Students can be exposed practically to Azure platform where they can work with cloud data services and learn real world data engineering processes.
Career-Focused
In Canada, the course of engineering is called Azures course, and its purpose is to furnish the learners with the skills or ability that is applicable in technology industry and enable them succeed in the competitive technologies industry.
Flexible Training
Version IT provides online training of Azure engineering in Canada where students and professionals can be able to attend the training at any place of their convenience.
Professional Mentoring
Experienced mentors provide students with step-by-step instructions and aid their learning process and assist them in establishing solid technical backgrounds.
Preparation of Future Cloud Careers
The training is aimed at the development of viable cloud data engineering that has great values to any given organization that is embracing Azure-based data platforms.
Career Paths After Azure Data Engineering Training
The completion of the Azure data engineering course in Canada opens up a range of high paying positions in technology. With the trend of increasing the use of cloud-based data systems by companies, qualified Azure experts remain a desirable asset.
Common career paths include:
- Azure Data Engineer
- Cloud Data Engineer
- Data Platform Engineer
- Azure Cloud solutions engineer.
- Information Systems Specialist.
- Analytics Data Engineer
Microsoft Azure data engineering training in Canada is a course that provides the technical expertise needed by the professionals to design and administer information platforms at the enterprise level.
Topics You will Learn
● Understanding different Cloud Models
● Advantages of Cloud Computing
● Different Cloud Services
● Different Cloud vendors in the market
● Introduction to Azure
● Azure cloud computing features
● Azure Services for Data Engineering
● Introduction of Azure Resources/Services with examples
● Azure management portal
● Advantage of Azure Cloud Computing
● Managing Azure resources with the Azure portal
● Overview of Azure Resource Manager
● Azure management services
● What is Azure Resource Groups
● Configuration and management of Azure Resource groups for hosting Azure
services
● Completed walkthrough of the Azure Portal with all the features
● What is Resource Groups and why do we need RG’s in Azure cloud computing
platform to host resources?
● Different types of Storage Accounts provisioning in Cloud computing with
different storage services
● Details explanation & understanding of different Blob/container storage
services
● Creating and managing the data in container storage services with Public and
Private accesses as per the need of a project
● Implementation of Snapshots for Blob storage services and File share storage
service
● Generating SAS for different storage services to make the storage content
browseable across all the globe or Publicly
● What is Standard Storage Account and Premium Storage account and which to
use accordingly as per the real time scenarios
● Detail explanation and implementation of Data Lake storage Gen2 Storage
Account to store the unstructured data in cloud storage services
● All the features/properties(Overview, activity log, Tags, Access control(IAM),
Storage browser…etc) of Azure Storage Accounts
● Maintenance and management of Storage keys and connection string for Azure
Storage services
● Implementing different levels of access(Reader, contributor, owners…etc) to the
Azure Storage accounts
● Moving the storage account with storage content across different Resources
Groups based on real time scenarios
● Migrating the data from On-prem(Private cloud) to Azure Storage account
(Public cloud) using Az copy(forward migration)
● Migrating the data from public cloud to Private cloud(reverse migration)
● Implementing the Az copy commands to migrate the data
● Moving the SA & its content from one Resource Group to another
● Azure Storage explorer for creating, managing, and maintaining the Azure
storage services data
● Installation of Azure Storage Explorer and what is the purpose of this tool for
Azure Storage accounts(its Purpose & benefits with real time scenarios)
● Generate Shared Access Signature(SAS) in Azure Storage Explorer(ASE) for
security implementation of Storage account content
● Managing of Access keys & connection strings of SA with Azure Storage
Explorer
● Configuration of Authentication and Authorization for Storage Account via
Azure Active Directory
● Hosting File share Storage services to On prem servers or Cloud Servers as
shared drive for File share servers
● Introduction to SQL DB’s
● Creation of new SQL DB’s & Sample SQL DB’s both in On-prem and Cloud
computing
● Planning and deploying Azure SQL Database
● Implementing and managing Azure SQL Database
● Managing Azure SQL Database security
● Planning and deployment of SQL DB’s in Azure cloud computing with real time
scenarios
● Different DB’s Deployment options
● Databases purchasing models.(VCore & DTU’s)
● Visualization of cloud DB server, Database, and validation of data from
on-prem(private cloud)
● Implementation of Firewall security rules on Azure DB servers to access and
connect from on-prem SSMS
● Creation of Database in on-premises and synch with azure cloud
● Migrating SQL DB’s from On-premises to Azure cloud computing using
Microsoft Data migration assistant
● Restoring SQL DB’s from On-prem to cloud computing
● Migration of Specific DB objects from on-prem to cloud based upon base upon
project requirements
● Implementation of RSV and scheduling the backups of SQL DB’s and Azure
Storage Account file share services on schedule, on demand based upon real
time scenarios
● Introduction to SQL DB Queries
● SQL queries detail explanations, syntax & execution based upon real time
scenarios
● Deep understanding and implementation of concepts/Components of ADF
● Building blocks of Azure Data Factory
● Complete features and walk through of Azure Data factory studio
● Different triggers and their implementation in ADF
● What is integration run time and different types of integration run time in ADF
● When to use ADF
● Why to use ADF
● Different types of ADF pipelines
● Pipelines in ADF
● Different types of Activities in ADF
● Datasets in Azure Data factory
● Linked services in ADF
● Copying the data from Blb Storage account to ADL’s Gen2 Storage account
● Copying of zip files(.csv) from Blob SA to ADL’s Gen2 SA using ADF
● Implementation and explanation of Metadata control in ADF to find the structure
before copying the data
● Implementation and explanation of Validation and If Condition
● Implementation of Get Metadata control, filter control & For Each Control or
activities in ADF
● Implementation & execution to copy the data from GitHub platform to Azure
Storage services with variables and parameters
● Implementation of Foreach control, copy data control and Set variable to
dynamically load the data from source to target using ADF
● Creating Dynamic pipelines with lookup activity to copy multiple .csv files data
picking form Json format data in Azure Storage services
● Copying the files from GitHub Dynamically with the use of Dynamic parameters
allocation-AUTOMATION PROCESS
● Copying the data from different files formats(.csv, .xlsx, .txt, .Parquet, .Json,
.SQL…etc) using suitable ADF controls/activities
● Implementation and execution of Loading the data from Blb SA to SQL DB single
table & multiple tables using copy data activity, ForEach activity
● Executing multiple pipelines in parallel with Execute pipeline activity
● Implementation of Schedule based triggers for different ADF pipelines
containing different activities.
● Implementation of Event based triggers for different ADF pipelines containing
different activities.
● Implementation of Thumbling window-based triggers for different ADF pipelines
containing different activities.
● Implementation and execution of storage and Event based triggers.
● Detail explanation & implementation of Azure Keyvaults
● Making the SQL DB connection string to store in Keyvault to enhance the
security for SA content and SQL DB
● Generating the secrets inside the Azure keyvault and granting access by
implementing the access policies for different users
● Detail walk through of GitHub portal
● Creating an account, repo’s, in GitHub portal
● Integrating Azure Data Factory with GitHub Portal as per project requirements.
● Placing, maintaining and executing the source code via GitHub portal for Azure
Data Factory.
● Creating master branch, practice branches in GitHub portal to merge the newly
created code via Pull Requests.
● Setting up the Repo for ADF pipelines and converting to live mode from GitHub
portal covering with real time scenarios.
● Designing new Data flows
● Designing and implementing transformations
● Inline Datasets in data flow source control
● Designing and implementing of Data flow with Source transformations, Filter
transformations & Sink transformations in ADF with inline Datasets
● Implementation of Select transformations with Data flows for various source
controls
● Implementation of Dataflows using Aggregate & Sink transformation
● Implementation of Dataflow with conditional split & Sink transformation with
copy data activity
● Implementation of Dataflow with Exists & Sink transformation
● Implementation of Azure Dataflows for Derived column transformation with
Source & Sink transformation
● Implementation of Azure Dataflows to connect to SQL DB with Source & Sink
transformation
● Union & Union flow transformation implementation with ADF Data flows
● Implementation of Azure Dataflows to connect to SQL DB with Source & Sink
transformation
● Implementation of windows functions…like Rank() function, Dense_Rank()
function, Row_Number() function…etc.
● What is Apache Spark, details explanation and implementation of Apache Spark
● Illustration and Elaboration of Apache Spark Architecture
● Explanation of RDD & DAG
● Understanding of different Apache Spark components
● What are worker nodes and slaves nodes in Azure Data Bricks clusters
● Implementation of Azure Databricks cluster by considering different worker
nodes and slave nodes
● Different features and properties of Azure Data Bricks clusters
● Creating single node and multi nodes clusters
● Creation of Pyspark notebooks in Databricks cluster to fulfil different business
requirements
● What is Azure Synapse Analytics
● Implementation of Linked Services/Datasets in Synapse Analytics
● Implementation of dedicated SQL Pool inside Synapse Analytics
● Implementation of serverless SQL Pool inside Synapse Analytics
● Creation of Apache spark pool in Azure Synapse Analytics
● Writing SQL Script in Azure Synapse analytics to get the result set in tabular and
chart formats
● Visualizing the data in Synapse analytics in variety of different charts (like pie
charts, line charts, bar charts…. etc)
● Designing of Synapse Analytics pipelines by considering various activities as
per the business requirements
● Creation of Datasets, Linked services for Synapse Analytics pipelines
● Data analysis with serverless spark pools in Azure Synapse Analytics
● What is Apache spark in azure synapse analytics
● Designing and development of Apache spark pool in Azure synapse
● Creating Spark Databases and tables to load the data from source system and
analysing the data in Synapse analytics
● What is Azure Stream Analytics
● Purposes and usage of Stream Analytics in Azure cloud computing
● Benefits and advantages of stream analytics
● Architecture diagram of data flow in Azure stream analytics with other cloud
services
● Understanding & usage of browser-based Raspberry Pi simulator
● Deployment of IoT Hub services as an input for Stream analytics jobs
● Implementation & execution of stream analytics jobs and designing inputs and
outputs for IoT Hub and Datalake Gen2
● Writing SQL scripts to generate live streaming data and loading it in destination
FAQ's
Upon successful completion of the training of Azure Data Engineering, one can take up a number of positions including but not limited to:
- Azure Data Engineer
- Cloud Data Engineer
- Data Platform Engineer
- Big Data Engineer
- ETL Developer
- Data Solutions Architect
